Name
Title | ||
|---|---|---|
ThermalNet: A deep reinforcement learning-based combustion optimization system for coal-fired boiler. |
Abstract | ||
|---|---|---|
This paper presents a combustion optimization system for coal-fired boilers that includes a trade-off between emissions control and boiler efficiency. Designing an optimizer for this nonlinear, multiple-input multiple-output problem is challenging. This paper describes the development of an integrated combustion optimization system called ThermalNet, which is based on a deep Q-network (DQN) and a long short-term memory (LSTM) module. ThermalNet is a highly automated system consisting of an LSTM–ConvNet predictor and a DQN optimizer. The LSTM–ConvNet extracts the features of boiler behavior from the distributed control system (DCS) operational data of a supercritical thermal plant. The DQN reinforcement learning optimizer contributes to the online development of policies based on static and dynamic states. ThermalNet establishes a sequence of control actions that both reduce emissions and simultaneously enhance fuel utilization. The internal structure of the DQN optimizer demonstrates a greater representation capacity than does the shallow multilayer optimizer. The presented experiments indicate the effectiveness of the proposed optimization system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.engappai.2018.07.003 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Combustion optimization,DQN,LSTM,Reinforcement learning,DCS | Thermal power station,Process engineering,Combustion,Mathematical optimization,Nonlinear system,Computer science,Coal,Boiler (power generation),Distributed control system,Reinforcement learning | Journal |
Volume | ISSN | Citations |
74 | 0952-1976 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Cheng | 1 | 9 | 2.06 |
| Yuexin Huang | 2 | 0 | 0.34 |
| Bo Pang | 3 | 5795 | 451.00 |
| Weidong Zhang | 4 | 383 | 67.45 |